A novel biologically-inspired target detection method based on saliency analysis for synthetic aperture radar (SAR) imagery

Saliency Object Detection (SOD) models driven by the biologically-inspired Focus of Attention (FOA) mechanism can result in highly accurate saliency maps. However, their application in high-resolution Synthetic Aperture Radar (SAR) images entails a number of intractable problems due to complex backgrounds. In this paper, we propose a novel hierarchical self-diffusion saliency (HSDS) method for detecting vehicle targets in large scale SAR images. To reduce the influence of cluttered returns on saliency analysis, we learn a weight vector from the training set to capture optimal initial saliency of the superpixels during saliency diffusion. By accounting for the multiple sizes of background objects, the saliency analysis is implemented in multi-scale space, and a saliency fusion strategy employed to integrate the multi-scale saliency maps. Simulation experiments demonstrate that our proposed method can produce a more accurate and stable detection performance, with decreased false alarms, compared to benchmark approaches

Brain inspired cognitive systems 2008

Brain Inspired Cognitive Systems 2008 (June 24-27, 2008; So Lus, Brazil) brought together leading scientists and engineers who use analytic, syntactic and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods towards ever higher levels of cognitive competence. This book includes the papers presented at four major symposia: Part I - Cognitive Neuroscience Part II - Biologically Inspired Systems Part III - Neural Computation Part IV - Models of Consciousness

Non-support vectors become support vectors (H1 is the initial hyperplane. H3 is the final hyperplane. A1, A2, A3, A4, B1, B2, B3 and B4 are samples).

<p>Non-support vectors become support vectors (H1 is the initial hyperplane. H3 is the final hyperplane. A1, A2, A3, A4, B1, B2, B3 and B4 are samples).</p

Flow-chart of incremental semi-supervised learning.

<p>Flow-chart of incremental semi-supervised learning.</p

The distance between the semi-labeled sample and the hyperplane is far but the classification result is still wrong (H1 is the initial hyperplane. H3 is the final hyperplane. A1, A2, A3, A4, B1, B2, B3 and B4 are samples).

<p>The distance between the semi-labeled sample and the hyperplane is far but the classification result is still wrong (H1 is the initial hyperplane. H3 is the final hyperplane. A1, A2, A3, A4, B1, B2, B3 and B4 are samples).</p

Flow-chart of our classification algorithm based on incremental semi-supervised SVM.

<p>Flow-chart of our classification algorithm based on incremental semi-supervised SVM.</p

All the semi-labeled are introduced correctly but the accuracy rate declines (H1 is the initial hyperplane, H3 is the final hyperplane and H2 is the hyperplane at a certain moment in the learning process.

<p>A1, A2, A3, A4, B1, B2, B3 and B4 are samples. A* represents a testing sample in class A. Ag* and Bg* represent a group of testing samples in class A and class B, respectively).</p

Flow-chart of new labeled samples’ learning.

<p>Flow-chart of new labeled samples’ learning.</p

The accuracy rate of the testing set with different algorithms.

<p>The accuracy rate of the testing set with different algorithms.</p